Literature DB >> 26303152

Mobile Applications for Type 2 Diabetes Risk Estimation: a Systematic Review.

Nino Fijacko1, Petra Povalej Brzan, Gregor Stiglic.   

Abstract

Screening for chronical diseases like type 2 diabetes can be done using different methods and various risk tests. This study present a review of type 2 diabetes risk estimation mobile applications focusing on their functionality and availability of information on the underlying risk calculators. Only 9 out of 31 reviewed mobile applications, featured in three major mobile application stores, disclosed the name of risk calculator used for assessing the risk of type 2 diabetes. Even more concerning, none of the reviewed applications mentioned that they are collecting the data from users to improve the performance of their risk estimation calculators or offer users the descriptive statistics of the results from users that already used the application. For that purpose the questionnaires used for calculation of risk should be upgraded by including the information on the most recent blood sugar level measurements from users. Although mobile applications represent a great future potential for health applications, developers still do not put enough emphasis on informing the user of the underlying methods used to estimate the risk for a specific clinical condition.

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Year:  2015        PMID: 26303152     DOI: 10.1007/s10916-015-0319-y

Source DB:  PubMed          Journal:  J Med Syst        ISSN: 0148-5598            Impact factor:   4.460


  31 in total

1.  AUSDRISK: an Australian Type 2 Diabetes Risk Assessment Tool based on demographic, lifestyle and simple anthropometric measures.

Authors:  Lei Chen; Dianna J Magliano; Beverley Balkau; Stephen Colagiuri; Paul Z Zimmet; Andrew M Tonkin; Paul Mitchell; Patrick J Phillips; Jonathan E Shaw
Journal:  Med J Aust       Date:  2010-02-15       Impact factor: 7.738

2.  Analysis of mobile health applications for a broad spectrum of consumers: a user experience approach.

Authors:  Juan M García-Gómez; Isabel de la Torre-Díez; Javier Vicente; Montserrat Robles; Miguel López-Coronado; Joel J Rodrigues
Journal:  Health Informatics J       Date:  2014-03       Impact factor: 2.681

3.  Smartphone data as objective measures of bipolar disorder symptoms.

Authors:  Maria Faurholt-Jepsen; Mads Frost; Maj Vinberg; Ellen Margrethe Christensen; Jakob E Bardram; Lars Vedel Kessing
Journal:  Psychiatry Res       Date:  2014-03-13       Impact factor: 3.222

4.  The performance of the Finnish Diabetes Risk Score, a modified Finnish Diabetes Risk Score and a simplified Finnish Diabetes Risk Score in community-based cross-sectional screening of undiagnosed type 2 diabetes in the Philippines.

Authors:  Grace M V Ku; Guy Kegels
Journal:  Prim Care Diabetes       Date:  2013-08-15       Impact factor: 2.459

5.  Validating the CANRISK prognostic model for assessing diabetes risk in Canada's multi-ethnic population.

Authors:  C A Robinson; G Agarwal; K Nerenberg
Journal:  Chronic Dis Inj Can       Date:  2011-12

6.  Design and development of a mobile computer application to reengineer workflows in the hospital and the methodology to evaluate its effectiveness.

Authors:  Andreas Holzinger; Primoz Kosec; Gerold Schwantzer; Matjaz Debevc; Rainer Hofmann-Wellenhof; Julia Frühauf
Journal:  J Biomed Inform       Date:  2011-07-26       Impact factor: 6.317

7.  Circulating adhesion molecules VCAM-1, ICAM-1, and E-selectin in carotid atherosclerosis and incident coronary heart disease cases: the Atherosclerosis Risk In Communities (ARIC) study.

Authors:  S J Hwang; C M Ballantyne; A R Sharrett; L C Smith; C E Davis; A M Gotto; E Boerwinkle
Journal:  Circulation       Date:  1997-12-16       Impact factor: 29.690

Review 8.  Prevalence of age-related maculopathy in Australia. The Blue Mountains Eye Study.

Authors:  P Mitchell; W Smith; K Attebo; J J Wang
Journal:  Ophthalmology       Date:  1995-10       Impact factor: 12.079

9.  The diabetes risk score: a practical tool to predict type 2 diabetes risk.

Authors:  Jaana Lindström; Jaakko Tuomilehto
Journal:  Diabetes Care       Date:  2003-03       Impact factor: 19.112

10.  Measuring physical activity in a cardiac rehabilitation population using a smartphone-based questionnaire.

Authors:  Leila Pfaeffli; Ralph Maddison; Yannan Jiang; Lance Dalleck; Marie Löf
Journal:  J Med Internet Res       Date:  2013-03-22       Impact factor: 5.428

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  5 in total

1.  Remotely Conducted App-Based Intervention for Cardiovascular Disease and Diabetes Risk Awareness and Prevention: Single-Group Feasibility Trial.

Authors:  Vera Helen Buss; Marlien Varnfield; Mark Harris; Margo Barr
Journal:  JMIR Hum Factors       Date:  2022-07-01

2.  Quality of Mobile Phone and Tablet Mobile Apps for Speech Sound Disorders: Protocol for an Evidence-Based Appraisal.

Authors:  Lisa M Furlong; Meg E Morris; Shane Erickson; Tanya A Serry
Journal:  JMIR Res Protoc       Date:  2016-11-29

3.  Early detection of type 2 diabetes mellitus using machine learning-based prediction models.

Authors:  Leon Kopitar; Primoz Kocbek; Leona Cilar; Aziz Sheikh; Gregor Stiglic
Journal:  Sci Rep       Date:  2020-07-20       Impact factor: 4.379

4.  Determining minimum set of features for diabetes mobile apps.

Authors:  Raheleh Salari; Sharareh R Niakan Kalhori; Farhad Fatehi; Marjan Ghazisaeedi; Mahin Nazari
Journal:  J Diabetes Metab Disord       Date:  2019-06-27

5.  A non-linear ensemble model-based surgical risk calculator for mixed data from multiple surgical fields.

Authors:  Ruoyu Liu; Xin Lai; Jiayin Wang; Xuanping Zhang; Xiaoyan Zhu; Paul B S Lai; Ci-Ren Guo
Journal:  BMC Med Inform Decis Mak       Date:  2021-07-30       Impact factor: 2.796

  5 in total

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